N-FINDR algorithm were employed for endmember extraction for decomposing the mixed pixels

which searches for each pixel from the dimension reduced feature space induced using principal component transformation or maximum noise factor transformation method.Due to the large search range for the endmembers

the efficiency of the N-FINDR algorithm is low.In this paper

we proposed the improved fast N-FINDR algorithm aiming to decrease the computation cost by providing a relative smaller search range

i.e.the candidate endmember set which was only a subset of the entire feature space.N-FINDR algorithm assumed that all the endmembers located at the vertexes of the simplex

which means that these pixels should be far away from the central part of all the pixels.Therefore

the percentile of chi-square distribution can be used to segment out these possible endmembers into a candidate set

which has much smaller size.The performance of the proposed algorithm has been verified using both synthetic and real hyperspectral data.Under the same endmember extraction precision

the modified N-FINDR algorithm has faster computation velocity and a higher overall efficiency.